Overview

Dataset statistics

Number of variables16
Number of observations574882
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.7 MiB
Average record size in memory165.4 B

Variable types

NUM9
CAT5
BOOL2

Reproduction

Analysis started2023-02-04 09:13:31.793784
Analysis finished2023-02-04 09:14:33.370084
Duration1 minute and 1.58 second
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

evil has constant value "0" Constant
stackAddresses has a high cardinality: 87166 distinct values High cardinality
args has a high cardinality: 184128 distinct values High cardinality
threadId is highly correlated with processIdHigh correlation
processId is highly correlated with threadIdHigh correlation
returnValue is highly skewed (γ1 = 24.59200612) Skewed
parentProcessId has 16318 (2.8%) zeros Zeros
userId has 569884 (99.1%) zeros Zeros
returnValue has 398081 (69.2%) zeros Zeros

Variables

timestamp
Real number (ℝ≥0)

Distinct count574807
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1760.699406745824
Minimum132.560721
Maximum3954.587643
Zeros0
Zeros (%)0.0%
Memory size4.4 MiB
2023-02-04T14:44:33.686465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum132.560721
5-th percentile140.6630085
Q1831.4504055
median1704.79921
Q32747.376426
95-th percentile3695.358346
Maximum3954.587643
Range3822.026922
Interquartile range (IQR)1915.92602

Descriptive statistics

Standard deviation1145.659134
Coefficient of variation (CV)0.6506841143
Kurtosis-1.165530394
Mean1760.699407
Median Absolute Deviation (MAD)934.4803985
Skewness0.2536834814
Sum1012194396
Variance1312534.851
2023-02-04T14:44:33.801377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
404.2397982< 0.1%
 
404.2616282< 0.1%
 
135.6790972< 0.1%
 
404.0641382< 0.1%
 
916.5397722< 0.1%
 
133.871432< 0.1%
 
903.4021842< 0.1%
 
409.5111932< 0.1%
 
404.0460042< 0.1%
 
3322.9027652< 0.1%
 
Other values (574797)574862> 99.9%
 
ValueCountFrequency (%) 
132.5607211< 0.1%
 
132.5607611< 0.1%
 
132.5608141< 0.1%
 
132.5608381< 0.1%
 
132.560881< 0.1%
 
ValueCountFrequency (%) 
3954.5876431< 0.1%
 
3954.5875261< 0.1%
 
3954.587481< 0.1%
 
3954.5874291< 0.1%
 
3954.5872541< 0.1%
 

processId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count316
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6676.543015784108
Minimum1
Maximum7676
Zeros0
Zeros (%)0.0%
Memory size4.4 MiB
2023-02-04T14:44:33.981436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile159
Q17303
median7355
Q37396
95-th percentile7488
Maximum7676
Range7675
Interquartile range (IQR)93

Descriptive statistics

Standard deviation2110.103806
Coefficient of variation (CV)0.3160473617
Kurtosis5.457933053
Mean6676.543016
Median Absolute Deviation (MAD)46
Skewness-2.724247019
Sum3838224402
Variance4452538.07
2023-02-04T14:44:34.106937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
159205173.6%
 
1163182.8%
 
737371191.2%
 
737165121.1%
 
733561321.1%
 
737460781.1%
 
733660751.1%
 
738159461.0%
 
737559301.0%
 
737054600.9%
 
Other values (306)48879585.0%
 
ValueCountFrequency (%) 
1163182.8%
 
54< 0.1%
 
76< 0.1%
 
86< 0.1%
 
806< 0.1%
 
ValueCountFrequency (%) 
767684< 0.1%
 
7675109< 0.1%
 
766910920.2%
 
766411120.2%
 
765710920.2%
 

threadId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count361
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6683.846718109107
Minimum1
Maximum7676
Zeros0
Zeros (%)0.0%
Memory size4.4 MiB
2023-02-04T14:44:34.215637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile159
Q17303
median7355
Q37396
95-th percentile7488
Maximum7676
Range7675
Interquartile range (IQR)93

Descriptive statistics

Standard deviation2096.105639
Coefficient of variation (CV)0.3136076764
Kurtosis5.564799239
Mean6683.846718
Median Absolute Deviation (MAD)46
Skewness-2.742106188
Sum3842423169
Variance4393658.849
2023-02-04T14:44:34.323597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
159204213.6%
 
1163182.8%
 
737371191.2%
 
737165121.1%
 
733561321.1%
 
737460781.1%
 
733660751.1%
 
738159461.0%
 
737559331.0%
 
737054621.0%
 
Other values (351)48888685.0%
 
ValueCountFrequency (%) 
1163182.8%
 
54< 0.1%
 
76< 0.1%
 
86< 0.1%
 
806< 0.1%
 
ValueCountFrequency (%) 
767684< 0.1%
 
7675109< 0.1%
 
76742< 0.1%
 
766910920.2%
 
766411120.2%
 

parentProcessId
Real number (ℝ≥0)

ZEROS

Distinct count50
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1348.6604938056853
Minimum0
Maximum7455
Zeros16318
Zeros (%)2.8%
Memory size4.4 MiB
2023-02-04T14:44:34.444282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1187
median1385
Q31640
95-th percentile4489
Maximum7455
Range7455
Interquartile range (IQR)1453

Descriptive statistics

Standard deviation1192.571458
Coefficient of variation (CV)0.8842636552
Kurtosis4.322130601
Mean1348.660494
Median Absolute Deviation (MAD)256
Skewness1.741538498
Sum775320642
Variance1422226.682
2023-02-04T14:44:34.538880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1878787115.3%
 
14696227410.8%
 
13365782610.1%
 
1552949.6%
 
1649492908.6%
 
1317469788.2%
 
4489456847.9%
 
1640428487.5%
 
1385219203.8%
 
1648187043.3%
 
Other values (40)8619315.0%
 
ValueCountFrequency (%) 
0163182.8%
 
1552949.6%
 
28370.1%
 
1878787115.3%
 
18835760.6%
 
ValueCountFrequency (%) 
745521840.4%
 
743718< 0.1%
 
743150< 0.1%
 
738918< 0.1%
 
738350< 0.1%
 

userId
Real number (ℝ≥0)

ZEROS

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.876898215633817
Minimum0
Maximum103
Zeros569884
Zeros (%)99.1%
Memory size4.4 MiB
2023-02-04T14:44:34.680630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.36378214
Coefficient of variation (CV)10.67829991
Kurtosis110.046136
Mean0.8768982156
Median Absolute Deviation (MAD)0
Skewness10.58499925
Sum504113
Variance87.68041596
2023-02-04T14:44:34.806466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
056988499.1%
 
10133980.6%
 
10011480.2%
 
1024410.1%
 
10311< 0.1%
 
ValueCountFrequency (%) 
056988499.1%
 
10011480.2%
 
10133980.6%
 
1024410.1%
 
10311< 0.1%
 
ValueCountFrequency (%) 
10311< 0.1%
 
1024410.1%
 
10133980.6%
 
10011480.2%
 
056988499.1%
 

mountNamespace
Real number (ℝ≥0)

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4026531905.873282
Minimum4026531840
Maximum4026532288
Zeros0
Zeros (%)0.0%
Memory size4.4 MiB
2023-02-04T14:44:34.932085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4026531840
5-th percentile4026531840
Q14026531840
median4026531840
Q34026531840
95-th percentile4026532217
Maximum4026532288
Range448
Interquartile range (IQR)0

Descriptive statistics

Standard deviation143.4487702
Coefficient of variation (CV)3.562588689e-08
Kurtosis0.9595569234
Mean4026531906
Median Absolute Deviation (MAD)0
Skewness1.719447344
Sum2.314780715e+15
Variance20577.54968
2023-02-04T14:44:35.033121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
402653184047473882.6%
 
40265322179455516.4%
 
402653223233980.6%
 
402653223111480.2%
 
40265322886020.1%
 
40265322294410.1%
 
ValueCountFrequency (%) 
402653184047473882.6%
 
40265322179455516.4%
 
40265322294410.1%
 
402653223111480.2%
 
402653223233980.6%
 
ValueCountFrequency (%) 
40265322886020.1%
 
402653223233980.6%
 
402653223111480.2%
 
40265322294410.1%
 
40265322179455516.4%
 

processName
Categorical

Distinct count34
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
ps
406313
systemd-udevd
94555
systemd-journal
 
20421
systemd
 
16318
systemd-tmpfile
 
13014
Other values (29)
 
24261
ValueCountFrequency (%) 
ps40631370.7%
 
systemd-udevd9455516.4%
 
systemd-journal204213.6%
 
systemd163182.8%
 
systemd-tmpfile130142.3%
 
amazon-ssm-agen48500.8%
 
snapd41870.7%
 
cron40800.7%
 
systemd-resolve33980.6%
 
systemd-user-ru12760.2%
 
Other values (24)64701.1%
 
2023-02-04T14:44:35.159904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length15
Median length2
Mean length5.068948062
Min length2

hostName
Categorical

Distinct count8
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
ubuntu
94996
ip-10-100-1-57
91938
ip-10-100-1-120
80082
ip-10-100-1-173
74344
ip-10-100-1-28
74211
Other values (3)
159311
ValueCountFrequency (%) 
ubuntu9499616.5%
 
ip-10-100-1-579193816.0%
 
ip-10-100-1-1208008213.9%
 
ip-10-100-1-1737434412.9%
 
ip-10-100-1-287421112.9%
 
ip-10-100-1-557215412.6%
 
ip-10-100-1-347167312.5%
 
ip-10-100-1-79154842.7%
 
2023-02-04T14:44:35.285883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.94666732
Min length6

eventId
Real number (ℝ≥0)

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.60531552562094
Minimum3
Maximum1010
Zeros0
Zeros (%)0.0%
Memory size4.4 MiB
2023-02-04T14:44:35.395915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median257
Q3257
95-th percentile1005
Maximum1010
Range1007
Interquartile range (IQR)254

Descriptive statistics

Standard deviation399.6046965
Coefficient of variation (CV)1.250306791
Kurtosis-0.7460730517
Mean319.6053155
Median Absolute Deviation (MAD)254
Skewness0.9838438043
Sum183735343
Variance159683.9135
2023-02-04T14:44:35.851022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25716448428.6%
 
316425728.6%
 
100513048622.7%
 
5435987.6%
 
4391886.8%
 
2175751.3%
 
100361361.1%
 
21738930.7%
 
633650.6%
 
6223840.4%
 
Other values (20)95161.7%
 
ValueCountFrequency (%) 
316425728.6%
 
4391886.8%
 
5435987.6%
 
633650.6%
 
2175751.3%
 
ValueCountFrequency (%) 
10107930.1%
 
1006141< 0.1%
 
100513048622.7%
 
10044040.1%
 
100361361.1%
 

eventName
Categorical

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
openat
164484
close
164257
security_file_open
130486
fstat
43598
stat
39188
Other values (25)
 
32869
ValueCountFrequency (%) 
openat16448428.6%
 
close16425728.6%
 
security_file_open13048622.7%
 
fstat435987.6%
 
stat391886.8%
 
access75751.3%
 
cap_capable61361.1%
 
getdents6438930.7%
 
lstat33650.6%
 
kill23840.4%
 
Other values (20)95161.7%
 
2023-02-04T14:44:35.960861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length21
Median length6
Mean length8.322031304
Min length4

stackAddresses
Categorical

HIGH CARDINALITY

Distinct count87166
Unique (%)15.2%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
[]
388340
[139692889307527]
 
2624
[139692889305126]
 
2185
[140214269716871]
 
1366
[140225374566791]
 
1259
Other values (87161)
179108
ValueCountFrequency (%) 
[]38834067.6%
 
[139692889307527]26240.5%
 
[139692889305126]21850.4%
 
[140214269716871]13660.2%
 
[140225374566791]12590.2%
 
[139743267879303]12080.2%
 
[140225374564390]11750.2%
 
[139743267876902]11300.2%
 
[140214269714470]10980.2%
 
[140225374561929]10410.2%
 
Other values (87156)17345630.2%
 
2023-02-04T14:44:36.266654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length340
Median length2
Mean length14.38523732
Min length2

argsNum
Real number (ℝ≥0)

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.74881453933155
Minimum0
Maximum5
Zeros793
Zeros (%)0.1%
Memory size4.4 MiB
2023-02-04T14:44:36.370104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median4
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.355567577
Coefficient of variation (CV)0.4931462482
Kurtosis-1.741222967
Mean2.748814539
Median Absolute Deviation (MAD)0
Skewness-0.2642315733
Sum1580244
Variance1.837563456
2023-02-04T14:44:36.448261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
429574251.4%
 
117151329.8%
 
29773317.0%
 
376041.3%
 
514970.3%
 
07930.1%
 
ValueCountFrequency (%) 
07930.1%
 
117151329.8%
 
29773317.0%
 
376041.3%
 
429574251.4%
 
ValueCountFrequency (%) 
514970.3%
 
429574251.4%
 
376041.3%
 
29773317.0%
 
117151329.8%
 

returnValue
Real number (ℝ)

SKEWED
ZEROS

Distinct count321
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.80929477701511
Minimum-115
Maximum7676
Zeros398081
Zeros (%)69.2%
Memory size4.4 MiB
2023-02-04T14:44:36.542607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-115
5-th percentile0
Q10
median0
Q36
95-th percentile9
Maximum7676
Range7791
Interquartile range (IQR)6

Descriptive statistics

Standard deviation278.1810723
Coefficient of variation (CV)18.78422143
Kurtosis621.8681189
Mean14.80929478
Median Absolute Deviation (MAD)0
Skewness24.59200612
Sum8513597
Variance77384.70897
2023-02-04T14:44:36.636821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
039808169.2%
 
910484918.2%
 
6157852.7%
 
3129252.2%
 
15123932.2%
 
-2120482.1%
 
3230670.5%
 
423190.4%
 
1217680.3%
 
513640.2%
 
Other values (311)102831.8%
 
ValueCountFrequency (%) 
-115124< 0.1%
 
-2230< 0.1%
 
-63730.1%
 
-3246< 0.1%
 
-2120482.1%
 
ValueCountFrequency (%) 
76761< 0.1%
 
76741< 0.1%
 
76691< 0.1%
 
76641< 0.1%
 
76571< 0.1%
 

args
Categorical

HIGH CARDINALITY

Distinct count184128
Unique (%)32.0%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
[{'name': 'fd', 'type': 'int', 'value': 9}]
104866
[{'name': 'fd', 'type': 'int', 'value': 6}]
 
16026
[{'name': 'fd', 'type': 'int', 'value': 3}]
 
12821
[{'name': 'fd', 'type': 'int', 'value': 15}]
 
12776
[{'name': 'fd', 'type': 'int', 'value': 32}]
 
3089
Other values (184123)
425304
ValueCountFrequency (%) 
[{'name': 'fd', 'type': 'int', 'value': 9}]10486618.2%
 
[{'name': 'fd', 'type': 'int', 'value': 6}]160262.8%
 
[{'name': 'fd', 'type': 'int', 'value': 3}]128212.2%
 
[{'name': 'fd', 'type': 'int', 'value': 15}]127762.2%
 
[{'name': 'fd', 'type': 'int', 'value': 32}]30890.5%
 
[{'name': 'fd', 'type': 'int', 'value': 6}, {'name': 'statbuf', 'type': 'struct stat*', 'value': '0x7FFDE8C1D530'}]27750.5%
 
[{'name': 'fd', 'type': 'int', 'value': 15}, {'name': 'statbuf', 'type': 'struct stat*', 'value': '0x7FFDE8C1D530'}]27730.5%
 
[{'name': 'fd', 'type': 'int', 'value': 4}]21900.4%
 
[{'name': 'fd', 'type': 'int', 'value': 12}]20120.3%
 
[{'name': 'fd', 'type': 'int', 'value': 5}]16150.3%
 
Other values (184118)41393972.0%
 
2023-02-04T14:44:37.391009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length445
Median length231
Mean length165.4473022
Min length2

sus
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
574609
1
 
273
ValueCountFrequency (%) 
0574609> 99.9%
 
1273< 0.1%
 

evil
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
574882
ValueCountFrequency (%) 
0574882100.0%
 

Interactions

2023-02-04T14:43:52.791513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:53.280986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:53.705960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:54.516526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:54.974168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:55.467122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:55.989912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:56.450568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:56.987201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:57.480965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:57.919361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:58.363331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:58.830974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:59.197118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:43:59.606483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:00.018555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:00.426053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:00.828985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:01.267861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:01.699236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:02.145168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:02.581200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:02.935104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:03.332303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:03.743763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:04.153757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:04.544233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:05.072189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:05.705463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:06.173264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:06.587903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:07.028952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:07.453778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:07.851791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:08.272213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:08.676081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:09.085202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:09.483354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:09.878652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:10.318116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:10.706182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:11.111251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:11.486487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:11.915819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:12.426356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:12.897625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:13.355229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:13.815235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:14.330781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:14.760074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:15.186844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:15.691792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:16.143428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:16.603867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:17.056733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:17.456969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:17.940544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:18.412361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:18.852686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:19.301491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:19.759372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:20.392394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:20.789710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:21.249506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:21.674119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:22.125875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:22.600027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:22.946203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:23.323054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:23.798228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:24.177068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:24.586439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:24.986566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:25.410096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:25.834181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:26.227399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:26.588969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:27.013468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:27.421212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:27.829989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:28.238693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-02-04T14:44:37.501176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-04T14:44:37.673859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-04T14:44:37.850822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-04T14:44:38.017763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-02-04T14:44:38.174725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-02-04T14:44:29.425007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-04T14:44:30.689041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

timestampprocessIdthreadIdparentProcessIduserIdmountNamespaceprocessNamehostNameeventIdeventNamestackAddressesargsNumreturnValueargssusevil
01809.495787381733711004026532231closeip-10-100-1-120157prctl[140662171848350, 11649800180280676]50[{'name': 'option', 'type': 'int', 'value': 'PR_SET_NAME'}, {'name': 'arg2', 'type': 'unsigned long', 'value': 94819493392601}, {'name': 'arg3', 'type': 'unsigned long', 'value': 94819493392601}, {'name': 'arg4', 'type': 'unsigned long', 'value': 140662171848350}, {'name': 'arg5', 'type': 'unsigned long', 'value': 140662156379904}]10
11809.495832381733711004026532231closeip-10-100-1-1203close[140662171777451]10[{'name': 'fd', 'type': 'int', 'value': 19}]10
21809.495921381733711004026532231closeip-10-100-1-1201010sched_process_exit[]00[]10
31894.13965173477347734104026531840ship-10-100-1-12021access[]2-2[{'name': 'pathname', 'type': 'const char*', 'value': '/etc/ld.so.preload'}, {'name': 'mode', 'type': 'int*', 'value': 'R_OK'}]10
41894.14212773477347734104026531840ship-10-100-1-1201005security_file_open[139778263990104, 139778263906698]40[{'name': 'pathname', 'type': 'const char*', 'value': '/etc/ld.so.cache'}, {'name': 'flags', 'type': 'int', 'value': 'O_RDONLY|O_LARGEFILE'}, {'name': 'dev', 'type': 'dev_t', 'value': 211812353}, {'name': 'inode', 'type': 'unsigned long', 'value': 62841}]10
51894.14258973477347734104026531840ship-10-100-1-120257openat[139778263990104, 139778263906698]43[{'name': 'dirfd', 'type': 'int', 'value': -100}, {'name': 'pathname', 'type': 'const char*', 'value': '/etc/ld.so.cache'}, {'name': 'flags', 'type': 'int', 'value': 'O_RDONLY|O_CLOEXEC'}, {'name': 'mode', 'type': 'int*', 'value': 2848309080}]10
61894.14275373477347734104026531840ship-10-100-1-1205fstat[]20[{'name': 'fd', 'type': 'int', 'value': 3}, {'name': 'statbuf', 'type': 'struct stat*', 'value': '0x7FFCAA471A40'}]10
71894.14332973477347734104026531840ship-10-100-1-1203close[]10[{'name': 'fd', 'type': 'int', 'value': 3}]10
81894.14340373477347734104026531840ship-10-100-1-1201005security_file_open[139778263990104, 139778263906765]40[{'name': 'pathname', 'type': 'const char*', 'value': '/usr/lib/x86_64-linux-gnu/libc-2.31.so'}, {'name': 'flags', 'type': 'int', 'value': 'O_RDONLY|O_LARGEFILE'}, {'name': 'dev', 'type': 'dev_t', 'value': 211812353}, {'name': 'inode', 'type': 'unsigned long', 'value': 3429}]10
91894.14385573477347734104026531840ship-10-100-1-120257openat[139778263990104, 139778263906765]43[{'name': 'dirfd', 'type': 'int', 'value': -100}, {'name': 'pathname', 'type': 'const char*', 'value': '/lib/x86_64-linux-gnu/libc.so.6'}, {'name': 'flags', 'type': 'int', 'value': 'O_RDONLY|O_CLOEXEC'}, {'name': 'mode', 'type': 'int*', 'value': 2848309080}]10

Last rows

timestampprocessIdthreadIdparentProcessIduserIdmountNamespaceprocessNamehostNameeventIdeventNamestackAddressesargsNumreturnValueargssusevil
5748721865.0755207469746918704026532217systemd-udevdubuntu257openat[139692889305126]415[{'name': 'dirfd', 'type': 'int', 'value': 6}, {'name': 'pathname', 'type': 'const char*', 'value': '..'}, {'name': 'flags', 'type': 'unsigned int', 'value': 'O_RDONLY|O_NOFOLLOW|O_CLOEXEC|O_PATH'}, {'name': 'mode', 'type': 'mode_t', 'value': 3372970022}]00
5748731865.0756997468746818704026532217systemd-udevdubuntu5fstat[]20[{'name': 'fd', 'type': 'int', 'value': 15}, {'name': 'statbuf', 'type': 'struct stat*', 'value': '0x7FFDE8C1D530'}]00
5748741865.0758787467746718704026532217systemd-udevdubuntu257openat[]415[{'name': 'dirfd', 'type': 'int', 'value': 6}, {'name': 'pathname', 'type': 'const char*', 'value': 'id'}, {'name': 'flags', 'type': 'unsigned int', 'value': 'O_RDONLY|O_NOFOLLOW|O_CLOEXEC|O_PATH'}, {'name': 'mode', 'type': 'mode_t', 'value': 3372970022}]00
5748751865.0759227464746418704026532217systemd-udevdubuntu5fstat[]20[{'name': 'fd', 'type': 'int', 'value': 15}, {'name': 'statbuf', 'type': 'struct stat*', 'value': '0x7FFDE8C1D530'}]00
5748761865.0760387471747118704026532217systemd-udevdubuntu3close[]10[{'name': 'fd', 'type': 'int', 'value': 6}]00
5748771865.0762177470747018704026532217systemd-udevdubuntu257openat[]46[{'name': 'dirfd', 'type': 'int', 'value': 15}, {'name': 'pathname', 'type': 'const char*', 'value': '..'}, {'name': 'flags', 'type': 'unsigned int', 'value': 'O_RDONLY|O_NOFOLLOW|O_CLOEXEC|O_PATH'}, {'name': 'mode', 'type': 'mode_t', 'value': 3372970022}]00
5748781865.0764137473747318704026532217systemd-udevdubuntu257openat[]415[{'name': 'dirfd', 'type': 'int', 'value': 6}, {'name': 'pathname', 'type': 'const char*', 'value': 'id'}, {'name': 'flags', 'type': 'unsigned int', 'value': 'O_RDONLY|O_NOFOLLOW|O_CLOEXEC|O_PATH'}, {'name': 'mode', 'type': 'mode_t', 'value': 3372970022}]00
5748791865.0765507468746818704026532217systemd-udevdubuntu3close[]10[{'name': 'fd', 'type': 'int', 'value': 6}]00
5748801865.0766057469746918704026532217systemd-udevdubuntu3close[]10[{'name': 'fd', 'type': 'int', 'value': 6}]00
5748811865.0767347464746418704026532217systemd-udevdubuntu3close[]10[{'name': 'fd', 'type': 'int', 'value': 6}]00